Metabolically structured population models: a unifying framework for microbial ecology and evolution
Koffel, Thomas ; Grimaud, Ghjuvan LU
; Litchman, Elena
and Klausmeier, Christopher A.
(2026)
In Journal of Theoretical Biology
624.
- Abstract
- Cells grow by acquiring external resources and transforming them internally, forming new cells as they divide. Metabolic networks focus on the flow of such resources within the cell as they undergo series of biochemical reactions. How population growth emerges from these complex dynamical networks remains unclear. Modeling the emergence of population growth, a central ecological concept, is thus essential to understand the forces shaping microbial communities. Here we present a novel theoretical framework that builds on structured population theory to model the growth of cell populations whose intracellular dynamics are driven by arbitrarily complex metabolic networks. Population growth is driven by limitation regimes, which capture how... (More)
- Cells grow by acquiring external resources and transforming them internally, forming new cells as they divide. Metabolic networks focus on the flow of such resources within the cell as they undergo series of biochemical reactions. How population growth emerges from these complex dynamical networks remains unclear. Modeling the emergence of population growth, a central ecological concept, is thus essential to understand the forces shaping microbial communities. Here we present a novel theoretical framework that builds on structured population theory to model the growth of cell populations whose intracellular dynamics are driven by arbitrarily complex metabolic networks. Population growth is driven by limitation regimes, which capture how reaction-level limitations combine in the network to determine growth rate. Resource availability changes trigger switches between limitation regimes, capturing resource interaction and colimitation. We also discovered alternative metabolic states, where different regimes are reached depending on initial metabolite concentrations. We first use a minimal metabolic network of limitation by two essential resources to illustrate our framework, then apply it to E. coli’s glycolysis pathway to showcase its capabilities on a more realistic, albeit still simplistic, network. By integrating metabolic networks into ecological theories, our work provides a mechanistic foundation for understanding the structure and evolution of microbial communities. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/95d74402-5130-4877-b215-828f720640e5
- author
- Koffel, Thomas
; Grimaud, Ghjuvan
LU
; Litchman, Elena
and Klausmeier, Christopher A.
- organization
- publishing date
- 2026
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Theoretical Biology
- volume
- 624
- article number
- 112410
- pages
- 10 pages
- publisher
- Academic Press
- external identifiers
-
- pmid:41690572
- ISSN
- 1095-8541
- DOI
- 10.1016/j.jtbi.2026.112410
- language
- English
- LU publication?
- yes
- id
- 95d74402-5130-4877-b215-828f720640e5
- date added to LUP
- 2026-02-16 09:40:27
- date last changed
- 2026-02-19 16:01:45
@article{95d74402-5130-4877-b215-828f720640e5,
abstract = {{Cells grow by acquiring external resources and transforming them internally, forming new cells as they divide. Metabolic networks focus on the flow of such resources within the cell as they undergo series of biochemical reactions. How population growth emerges from these complex dynamical networks remains unclear. Modeling the emergence of population growth, a central ecological concept, is thus essential to understand the forces shaping microbial communities. Here we present a novel theoretical framework that builds on structured population theory to model the growth of cell populations whose intracellular dynamics are driven by arbitrarily complex metabolic networks. Population growth is driven by limitation regimes, which capture how reaction-level limitations combine in the network to determine growth rate. Resource availability changes trigger switches between limitation regimes, capturing resource interaction and colimitation. We also discovered alternative metabolic states, where different regimes are reached depending on initial metabolite concentrations. We first use a minimal metabolic network of limitation by two essential resources to illustrate our framework, then apply it to E. coli’s glycolysis pathway to showcase its capabilities on a more realistic, albeit still simplistic, network. By integrating metabolic networks into ecological theories, our work provides a mechanistic foundation for understanding the structure and evolution of microbial communities.}},
author = {{Koffel, Thomas and Grimaud, Ghjuvan and Litchman, Elena and Klausmeier, Christopher A.}},
issn = {{1095-8541}},
language = {{eng}},
publisher = {{Academic Press}},
series = {{Journal of Theoretical Biology}},
title = {{Metabolically structured population models: a unifying framework for microbial ecology and evolution}},
url = {{http://dx.doi.org/10.1016/j.jtbi.2026.112410}},
doi = {{10.1016/j.jtbi.2026.112410}},
volume = {{624}},
year = {{2026}},
}